An adaptive deep learning method for item recommendation system

Da’u, Aminu; Salim, Naomie; Rabiu, Idris

doi:10.1016/j.knosys.2020.106681

Cited by 40 publications

(11 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…e system extracts the highest part of the item and recommends it to students. In this way, personalized recommendation of English teaching resources is realized [15].…”

Section: E Realization Of Personalized Recommendation Of English Teac...mentioning

confidence: 99%

A Personalized Recommendation System for English Teaching Resources Based on Multi-K Nearest Neighbor Regression Algorithm

Tang¹,

Yu²

2022

Security and Communication Networks

View full text Add to dashboard Cite

In order to ensure the quality of resource recommendation and solve the problems of low recommendation accuracy, long recommendation time, and high data loss rate in the process of resource recommendation in traditional methods, a personalized recommendation system of English teaching resources based on the multi-K nearest neighbor regression algorithm is designed. According to the overall architecture of the personalized recommendation system of teaching resources, this study designs the resource browsing function module, teaching resource detailed page recommendation module, and teaching resource database. Based on the basic idea of the multi-K nearest neighbor regression algorithm, in order to avoid the loss of important data in English teaching resource recommendation and reduce the data loss rate, a missing data reconstruction algorithm of English teaching resources is proposed. Finally, the path interest of student users is considered from the selection of browsing path and access time to realize the personalized recommendation of English teaching resources. The experimental results show that the system has high resource recommendation accuracy, short recommendation time, and low data loss rate.

show abstract

“…e system extracts the highest part of the item and recommends it to students. In this way, personalized recommendation of English teaching resources is realized [15].…”

Section: E Realization Of Personalized Recommendation Of English Teac...mentioning

confidence: 99%

A Personalized Recommendation System for English Teaching Resources Based on Multi-K Nearest Neighbor Regression Algorithm

Tang¹,

Yu²

2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…Particularly, the advent of CNN and RNN has been a great success in many natural language processing tasks. So, people started to experiment with deep learning methods, DeepCoNN, D-Attn, etc., to mine user preferences and make recommendations from the user's perspective, which can be directly applied to predictive scoring [25]. DeepCoNN is composed of two parallel neural networks with CNN as the basic model, learning the representation of the learner and the thought-politics-cultural preferences of interest, respectively, and connecting the two sections at the top of the network to see the interaction, which demonstrates the validity of user-focused texts on thought-politics education for alleviating the sparsity problem.…”

Section: E Function Of Deep Learning In Enhancing Ought-politics Educ...mentioning

confidence: 99%

Artificial Intelligence Technology and Its Application in Improving Thought-Politics Education

Bai

Wang

2022

Mobile Information Systems

View full text Add to dashboard Cite

As socialism with Chinese characteristics enters a new age, thought-politics education has also entered a new phase of development. Facing new opportunities and challenges, attaching importance to quality, pursuing quality, and improving quality have become the key directions for the development of thought-politics education. In the paper, a CNN-BiLSTM-based recommendation algorithm is proposed and combined with AI-related algorithms to be used in the task of improving thought-politics education. First, deep feature extraction in temporal and spatial dimensions by using LSTM and convolutional networks, respectively; then, by using multiscale attention fusion mechanisms to enhance the expressiveness of the features and make recommendations with the help of multilayer perceptron. Through extensive experiments, it is verified that the model has higher recommendation performance in this paper while maintaining higher real-time performance. It is tested on real data sets to verify that the model in the paper has better robustness.

show abstract

“…An attention distribution directed information transmission network gets the lowest mean square error of 1.031% (Sun et al 2020a ). Deep learning models are applied to collect relevant characteristics from product reviews on musical instruments, and for the item recommendation job, the model obtains a mean absolute error of 9.04% (Dau et al 2021 ). The Word2Vec model recognizes an entity from Chinese news articles and performs public opinion orientation analysis with an accuracy of 87.23% for the product assessment and recommendation task (Wang et al 2019 ).…”

Section: Review On Text Analytics Word Embedding Application and Deep...mentioning

confidence: 99%

Impact of word embedding models on text analytics in deep learning environment: a review

2023

View full text Add to dashboard Cite

The selection of word embedding and deep learning models for better outcomes is vital. Word embeddings are an n-dimensional distributed representation of a text that attempts to capture the meanings of the words. Deep learning models utilize multiple computing layers to learn hierarchical representations of data. The word embedding technique represented by deep learning has received much attention. It is used in various natural language processing (NLP) applications, such as text classification, sentiment analysis, named entity recognition, topic modeling, etc. This paper reviews the representative methods of the most prominent word embedding and deep learning models. It presents an overview of recent research trends in NLP and a detailed understanding of how to use these models to achieve efficient results on text analytics tasks. The review summarizes, contrasts, and compares numerous word embedding and deep learning models and includes a list of prominent datasets, tools, APIs, and popular publications. A reference for selecting a suitable word embedding and deep learning approach is presented based on a comparative analysis of different techniques to perform text analytics tasks. This paper can serve as a quick reference for learning the basics, benefits, and challenges of various word representation approaches and deep learning models, with their application to text analytics and a future outlook on research. It can be concluded from the findings of this study that domain-specific word embedding and the long short term memory model can be employed to improve overall text analytics task performance.

show abstract

An adaptive deep learning method for item recommendation system

Cited by 40 publications

References 36 publications

A Personalized Recommendation System for English Teaching Resources Based on Multi-K Nearest Neighbor Regression Algorithm

A Personalized Recommendation System for English Teaching Resources Based on Multi-K Nearest Neighbor Regression Algorithm

Artificial Intelligence Technology and Its Application in Improving Thought-Politics Education

Impact of word embedding models on text analytics in deep learning environment: a review

Contact Info

Product

Resources

About